{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Basic Principles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Show different ways to present statistical data.\n",
    "\n",
    "This script is written in *MATLAB* or *IPpython* style, to show how\n",
    "best to use Python interactively.\n",
    "Note than in *IPython*, the ``show()`` commands are automatically generated.\n",
    "The examples contain:\n",
    "\n",
    "- scatter plots\n",
    "- histograms\n",
    "- KDE\n",
    "- errorbars\n",
    "- boxplots\n",
    "- probplots\n",
    "- cumulative density functions\n",
    "- regression fits\n",
    "\n",
    "Author: thomas haslwanter, March-2015"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Getting things ready"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "First, import the libraries that you are going to need. You could also do\n",
    "that later, but it is better style to do that at the beginning.\n",
    "pylab imports the numpy, scipy, and matplotlib.pyplot libraries into the\n",
    "current environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "import scipy.stats as stats\n",
    "import seaborn as sns\n",
    "sns.set_context('notebook')\n",
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# Generate data that are normally distributed\n",
    "x = randn(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/OxMTE7Vs2TKnD4sAivdGGl64vceu/bTZdAQwA5urAN3khdt7GNoGgoVxMKCbvLB3OUPb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\nhDYAAIYgtAEAMAShDQCAIQhtAAAM8f+TELkcvnLCXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x243216f12b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(x,'.')\n",
    "title('Scatter Plot')\n",
    "xlabel('X')\n",
    "ylabel('Y')\n",
    "draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x24320fd3550>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AWIJoAwBgCaINAIAliDYAAJYg2gAAWIJoAwBgCaINAIAliDYAAJYg2gAAWIJoAwBgCaIN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\nEG0AACxBtAEAsMT/A7aA+nWdOsljAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2432159f5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(x)\n",
    "xlabel('Data Values')\n",
    "ylabel('Frequency')\n",
    "title('Histogram, default settings')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = randn(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2432172c978>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DvR3Boxgvypo3X+OK9v5WpPH68lg9WuSvvPKKWrZsqREjRuj48eMaOHCgbDab8/aCggKF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\nHAAAg1HkAAAY7P8B4lWaWMCyM1wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x243216d1cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(x,25)\n",
    "xlabel('Data Values')\n",
    "ylabel('Frequency')\n",
    "title('Histogram, 25 bins')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### KDE\n",
    "\n",
    "Kernel Density Estimation (KDE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x243215c6e10>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VePjhh6EoCvR6PQwGAzQaXvhOdDU9/VaUnu1EXKQfIoJ8RMcRbtHw/uW5RY2CkxBNLqeN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24321769358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.kdeplot(x)\n",
    "xlabel('Data Values')\n",
    "ylabel('Density')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Cumulative probability density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x24320f4f9b0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FiIb7gF4QIYr3rL+zThTL1919rHjnsfY2CoQE1IeNCU6DW23hHhYWhuTkZMN9URQhk8kA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\nCCGExvwFeLrOOpLRCMYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24321b710b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numbins = 20\n",
    "cdf = stats.cumfreq(x,numbins)\n",
    "plot(cdf[0])\n",
    "xlabel('Data Values')\n",
    "ylabel('Cumulative Frequency')\n",
    "title('Cumulative probablity density function')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x24320fe1550>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x24320da06d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The error bars indicate 1.5* the inter-quartile-range (IQR), and the box consists of the\n",
    "# first, second (middle) and third quartile\n",
    "boxplot(x, sym='o')\n",
    "title('Boxplot')\n",
    "ylabel('Values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x24321737748>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qIQCgIXIkCwBJRBYAkogsACQRWQBIIrIAkERkASCJyO6nysrKuPzyy2P48OExatSo2LJl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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24320f02f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot(x, vert=False, sym='*')\n",
    "title('Boxplot, horizontal')\n",
    "xlabel('Values')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Errorbars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.2, 19)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WZclutxdZn5SUpBUrVigtLU09evTQmDFj3FkeAAAezWZZluXug2ZmZmrs2LEa\nNmyYnnzyScf4G2+8ofDwcPn6+urFF19UWFiYevbsWeq+8vML5OVlL3UbAABuB24P9YsXL+qFF15Q\neHi4hgwZ4hi3LEuZmZny8/OTJK1du1ZXr17VCy+8UOr+KvsnNSb9TMeUXkzpQ6KX6sqUXkzpQ6KX\nsvZXErd++/3y5cuKiopSTExMkUCXfrh6HzhwoLKysmRZlhITE/lsHQCAcnDrZ+rLli3T999/r6VL\nl2rp0qWSpKFDhyonJ0fDhw/XhAkTFBkZKW9vb3Xq1Endu3d3Z3kAAHi0KvlMvTIx/V4yU3oxpQ+J\nXqorU3oxpQ+JXsraX0m4+QwAAIYg1AEAMAShDgCAIQh1AAAMQagDAGAIQh0AAEMQ6gAAGIJQBwDA\nEIQ6AACGcOttYoGybNpzVgl7k8vc7qnOzRXStaXrCwIAD8KVOgAAhiDUAQAwBNPvqFZCurYsMq0e\ns3Sf7Hab5o3pVIVVAYBn4EodAABDEOoAABiCUEe1lXg8VVczc3UpPUfT/pyoxOOpVV0SAFRrfKaO\nainxeKqWJyQ5llPSshzLj917Z1WVBQDVGlfqqJa27E8uYfy8W+sAAE9CqKNa+uZydrHjF69kubkS\nAPAchDqqpbsa1i12vPEdPm6uBAA8B6GOamlAp+YljDdzbyEA4EH4ohyqpZtfhlu1+bgKCi0FBvhq\nQKdmfEkOAEpBqKPaeuzeO7X+n2dkt9v0h+hHq7ocAKj2mH4HAMAQhDoAAIZg+h3VSknPU4+at7PI\nMs9TB4BbcaUOAIAhCHUAAAzB9DuqlZ8/T12SAgL8lJaWUUUVAYDn4EodAABDEOoAABjCrdPvhYWF\nmjFjhr744gt5e3tr1qxZatbsx9t+xsfH691335WXl5fGjh2rnj17uq22xOOp2rI/Wd9cydZdd9TV\ngE7NuXsZAMCjuDXUt2/frry8PK1bt06HDx/WvHnz9NZbb0mS0tLSFBcXpw0bNig3N1fh4eHq3Lmz\nvL29XV4Xz+4GAJjAraF+8OBBde3aVZL04IMP6tixY451n3/+uR566CF5e3vL29tbQUFBOnnypNq1\na+eyeuatPagvv75W4vrlCUlanpCk1k1/pUlPd3BZHQAAVAa3hnpmZqZ8fX0dy3a7Xfn5+fLy8lJm\nZqb8/Pwc63x8fJSZmVnmPhs0qCsvL3uF6vGu6Vz73jW9FBDgV/aG1ZCn1v1zpvQh0Ut1ZUovpvQh\n0UtFuDWPr+SHAAAKYUlEQVTUfX19lZWV5VguLCyUl5dXseuysrKKhHxJ0tOzK1xP3o18p7fzxJ9U\nmfJTMFP6kOilujKlF1P6kOilrP2VxK2h3r59e33yySd64okndPjwYbVu3dqxrl27dlq8eLFyc3OV\nl5enM2fOFFnvCjen1H/+mfpNY566j8/UAQAew2ZZluWug9389vuXX34py7I0Z84c7d69W0FBQerV\nq5fi4+O1bt06WZalMWPGqG/fvu4qTbs/S9F7O07p69QMNb3TT0N7tVK3hwLddnwAAH4pt4Y6AABw\nHW4+AwCAIQh1AAAMQagDAGAIQh0AAEMQ6gAAGOK2DPXCwkJNmzZNw4cPV0REhM6fP19kfXx8vEJD\nQzVs2DB98sknVVSlc8rqZdasWQoNDVVERIQiIiKUkVH9b+Zw5MgRRURE3DK+c+dODR48WMOHD1d8\nfHwVVFY+JfXx17/+VQMGDHCck7Nnz1ZBdc65ceOGYmJiFB4eriFDhmjHjh1F1nvSOSmrF086LwUF\nBZo8ebJGjBihp59+Wl999VWR9Z50XsrqxZPOiyRduXJF3bt315kzZ4qMu+2cWLehjz76yIqNjbUs\ny7I+++wz67//+78d6y5dumQNHDjQys3Ntb7//nvH39VVab1YlmWNGDHCunLlSlWUViErVqywBg4c\naA0dOrTIeF5entW7d2/r6tWrVm5urhUaGmqlpaVVUZVlK6kPy7KsiRMnWkePHq2Cqspv/fr11qxZ\nsyzLsqz09HSre/fujnWedk5K68WyPOu8fPzxx9akSZMsy7Ksf/3rX0X+f+9p56W0XizLs85LXl6e\n9fzzz1uPP/64dfr06SLj7jont+WVurMPlvHz83M8WKa6Kq2XwsJCnT9/XtOmTdOIESO0fv36qirT\naUFBQVqyZMkt42fOnFFQUJB+9atfydvbWx06dNCnn35aBRU6p6Q+JCkpKUkrVqxQWFiYli9f7ubK\nyqdfv376n//5H0mSZVmy2398zoKnnZPSepE867z07t1bM2fOlCR98803atiwoWOdp52X0nqRPOu8\nzJ8/XyNGjFCjRo2KjLvznNyWoV7Sg2VurqvIg2WqSmm9ZGdna+TIkVqwYIFWrVqlv//979X6DYok\n9e3b1/E8gJ/ytPNSUh+SNGDAAM2YMUNvv/22Dh48WK0/4vHx8ZGvr68yMzM1fvx4vfTSS451nnZO\nSutF8qzzIkleXl6KjY3VzJkzi9x909POi1RyL5LnnJeNGzfK39/fcZH1U+48J7dlqLviwTJVpbRe\n6tSpo8jISNWpU0e+vr7q2LFjtQ/1knjaeSmJZVl65pln5O/vL29vb3Xv3l3Hjx+v6rJKdfHiRUVG\nRio4OFhPPvmkY9wTz0lJvXjieZF+uDL86KOPNHXqVGVn//BwK088L1LxvXjSedmwYYP27duniIgI\nnThxQrGxsUpLS5Pk3nNyW4Z6+/bttXv3bkkq9sEyBw8eVG5urjIyMtzyYJlforRekpOTFRYWpoKC\nAt24cUOHDh3SfffdV1Wl/iL/+Z//qfPnz+vq1avKy8vTgQMH9NBDD1V1WeWWmZmpgQMHKisrS5Zl\nKTExUW3btq3qskp0+fJlRUVFKSYmRkOGDCmyztPOSWm9eNp52bRpk2Mquk6dOrLZbKpR44f/nHva\neSmtF086L2vXrtWaNWsUFxenNm3aaP78+QoICJDk3nPi1qe0VRd9+vTR3r17NWLECMeDZf7yl784\nHiwTERGh8PBwWZalCRMmqFatWlVdconK6iU4OFjDhg1TzZo1FRwcrFatWlV1yeXy4YcfKjs7W8OH\nD9ekSZMUHR0ty7I0ePBg3Xmn5zxB76d9TJgwQZGRkfL29lanTp3UvXv3qi6vRMuWLdP333+vpUuX\naunSpZKkoUOHKicnx+POSVm9eNJ5efzxxzV58mQ9/fTTys/P1yuvvKKPP/7YI/+/UlYvnnRefq4q\n/vvFA10AADDEbTn9DgCAiQh1AAAMQagDAGAIQh0AAEMQ6gAAGIJQBzxcSkqK2rZtq+DgYAUHB6tv\n376aPHmyLl++XOZri3vgTEnee+89RUdH3zI+efJk/e1vfyvxdRs3btSkSZOcPg6AiiPUAQM0atRI\nH3zwgT744ANt27ZNDRs21Pjx48t83b///W+nj9G/f38dPnxYV65ccYzl5OTok08+KXJ3NgBVh1AH\nDGOz2TRu3DidOnVKJ0+eVH5+vqZMmaLhw4erV69eev7553X9+nXNmjVL0g83YJGkNWvWaOjQoRo4\ncKAGDRp0yyMufX191adPH23dutUxtn37dnXs2FENGjRQamqqoqOjNWzYMPXs2VN/+tOfbqntv/7r\nv5SSkiJJSkxMdMwUnD9/XqNGjdKgQYMUFhbmuBXohx9+qODgYIWGhmr8+PHKzc2t/H8wwCCEOmAg\nb29vNWvWTGfPntVnn32mmjVrat26dfr444+VkZGhXbt2acqUKZJ+mFbPzMzU9u3bFRcXp82bN6tH\njx5au3btLfsNDQ3V5s2bHcubNm3S4MGDJUmbN2/WwIEDFR8fr4SEBL399tv67rvvnKo3NjZWMTEx\nev/99zVz5kxNmDBBkrR48WKtXr1aGzduVJMmTar9s7SBqnZb3iYWuB3YbDbVrl1bjzzyiOrXr6+1\na9fq7NmzSk5Odjww4yZfX1+99tpr2rJli5KTk7Vnzx61adPmln0+8sgjSk9P19dff63atWsrOTlZ\nnTt3liRFR0frX//6l/785z/r1KlTunHjhnJycsqsMysrS8eOHdPkyZMdY9nZ2UpPT1fPnj0VFham\nXr16qW/fvsXWBOBHhDpgoLy8PJ07d06//vWvtWPHDr3++uuKjIxUaGio0tPT9fO7Q1+8eFEREREa\nOXKkunXrpoYNG+rEiRO37NdmsykkJESbN29W7dq19dRTTzkevjFv3jx9/fXXGjhwoHr37q19+/bd\nchxJjrGbjwguLCyUt7e3PvjgA8c23377rerXr68pU6bo5MmT2rVrl2JiYvTiiy8qODi40v6dANMw\n/Q4YprCwUEuWLNEDDzygoKAg7d+/X/3799fgwYNVr149JSYmqqCgQJJkt9uVn5+vo0ePqlmzZnr2\n2Wd1//33a/v27Y5tfm7QoEH6+OOPtW3bNoWGhjrG9+7dq+joaPXv31/nzp1TamqqCgsLi7y2QYMG\nOn36tCRpx44dkiQ/Pz81b97cEep79+51PNzj8ccfV4MGDTRmzBgFBwcX+0YDwI+4UgcMcOnSJccV\nbGFhodq0aaPXXntN0g9fhPvtb3+rLVu2qGbNmmrfvr3jy2o3n+QXHx+vd955R0888YQsy9Ijjzyi\nU6dOFXusxo0bq0GDBiosLFTTpk0d42PGjNHvfvc71a5dW//xH/+htm3bOo5z0/jx4zVz5ky98cYb\n6tKli2N8wYIFmjFjhlatWqWaNWvqj3/8o2rWrKnx48dr1KhRql27tu644w7NmzevUv/dANPwlDYA\nAAzB9DsAAIYg1AEAMAShDgCAIQh1AAAMQagDAGAIQh0AAEMQ6gAAGIJQBwDAEP8PSV3Kp33cEy4A\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1419fc6aac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = arange(5)\n",
    "y = x**2\n",
    "errorBar = x/2\n",
    "errorbar(x,y, yerr=errorBar, fmt='o', capsize=5, capthick=3)\n",
    "\n",
    "plt.xlabel('Data Values')\n",
    "plt.ylabel('Measurements')\n",
    "plt.title('Errorbars')\n",
    "\n",
    "xlim([-0.2, 4.2])\n",
    "ylim([-0.2, 19])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Check for Normality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x24320df7438>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hdLeOZB49xQgmUHbPjzwxMec5w2Ni/HLcLuKJnIb2jBkzeOeddyhVqhRXX301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\nWwghhLARfwFGRmeSjklboQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x243216c0ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visual check\n",
    "x = randn(100)\n",
    "_ = stats.probplot(x, plot=plt)\n",
    "title('Probplot - check for normality')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 2D Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x24320e67dd8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IQE9rz+sPRA32B4+2igaAWK1SuRO0egsEg3i1tg4ra3ZgxXM7sLJmB16trUMg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\n2n1BlCy2tHUmlaDi7q9V6F20pAy021B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      "text/plain": [
       "<matplotlib.figure.Figure at 0x24320e3dac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate data\n",
    "x = randn(200)\n",
    "y = 10+0.5*x+randn(len(x))\n",
    "\n",
    "# Scatter plot\n",
    "scatter(x,y)\n",
    "# This one is quite similar to \"plot(x,y,'.')\"\n",
    "title('Scatter plot of data')\n",
    "xlabel('X')\n",
    "ylabel('Y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## LineFit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ag9ecjWWLZuKkk4rgLS9A9oHR72dH5XTMX74B85dvwBsPNwyd4MyoEh55TnTM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\n/weKmmFawHKDrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1419fc13f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "M = vstack((ones(len(x)), x)).T\n",
    "pars = linalg.lstsq(M,y)[0]\n",
    "intercept = pars[0]\n",
    "slope = pars[1]\n",
    "scatter(x,y)\n",
    "plot(x, intercept + slope*x, 'r')\n",
    "show()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
